AIMC Topic: Machine Learning

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Feature Extraction of Athlete's Post-Match Psychological and Emotional Changes Based on Deep Learning.

Computational intelligence and neuroscience
Athletes have had to deal with significant shifts in the way they think about psychology and emotion before and after attending a match in their respective fields. It has become increasingly difficult for players of any sport to overcome these differ...

Application of Machine Learning to Predict Estimated Ultimate Recovery for Multistage Hydraulically Fractured Wells in Niobrara Shale Formation.

Computational intelligence and neuroscience
The completion design of multistage hydraulic fractured wells including the cluster spacing injected proppant and slurry volumes has shown a great influence on the well production rates and estimated ultimate recovery (EUR). EUR estimation is a criti...

Integrating artificial intelligence and natural language processing for computer-assisted reporting and report understanding in nuclear cardiology.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Natural language processing (NLP) offers many opportunities in Nuclear Cardiology. These opportunities include applications in converting nuclear cardiology imaging reports to digital searchable information that may be used as Big Data for machine le...

A novel tool that allows interactive screening of PubMed citations showed promise for the semi-automation of identification of Biomedical Literature.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVES: Systematic reviews form the basis of evidence-based medicine, but are expensive and time-consuming to produce. To address this burden, we have developed a literature identification system (Pythia) that combines the query fo...

Optimization of Imbalanced and Multidimensional Learning Under Bayes Minimum Risk and Savings Measure.

Big data
The full potential of data analysis is crippled by imbalanced and high-dimensional data, which makes these topics significantly important. Consequently, substantial research efforts have been directed to obtain dimension reduction and resolve data im...

Machine learning and artificial intelligence in cardiac transplantation: A systematic review.

Artificial organs
BACKGROUND: This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges ide...

Data-driven prediction in dynamical systems: recent developments.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
In recent years, we have witnessed a significant shift toward ever-more complex and ever-larger-scale systems in the majority of the grand societal challenges tackled in applied sciences. The need to comprehend and predict the dynamics of complex sys...

Machine learning-based statistical closure models for turbulent dynamical systems.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
We propose a machine learning (ML) non-Markovian closure modelling framework for accurate predictions of statistical responses of turbulent dynamical systems subjected to external forcings. One of the difficulties in this statistical closure problem ...

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.

Genome biology
The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurate...

A Novel Approach to Predict Brain Cancerous Tumor Using Transfer Learning.

Computational and mathematical methods in medicine
As the most prevalent and deadly malignancy, brain tumors have a dismal survival rate when they are at their most hazardous. Using mostly traditional medical image processing methods, segmenting and classifying brain malignant tumors is a challenging...